Dipl.-Inf. Marvin Lindner room: H-A-7110 phone: +49 271 / 740 - 2794 mail: lindner@fb12.uni-siegen.de address: Universität Siegen, Lehrstuhl für Computergrafik, 57068 Siegen

Teaching

• Graphikpraktikum 2009
• Graphikpraktikum 2008
• Graphikpraktikum 2007
• Graphikpraktikum 2006

Research

3D Vision

A growing number of modern applications such as position determination, object recognition and collision prevention depend on accurate scene analysis. The estimation of an object's distance relative to an observers position by image analysis or laser scan techniques is thereby still the most time-consuming and expansive part.

A lower-priced and much faster alternative is the distance measurement with modulated, coherent infrared light based on the Photonic Mixer Device (PMD) technique.

Distance Calibration

This reasearch direction focuses on the design and evaluation of proper calibration models for the very complex and lightly explored distance mapping process of the PMD, which includes lateral and distance calibration by deviation analysis. Additional concepts combine PMD information with common highresolution 2D images in order to increase the distance resolution and accuracy for further image processing tasks in the context of computer vision.

Data Fusion

An important field of reasearch in computer vision is the 3D analysis and reconstruction of objects and scenes. Unfortunately, PMD-based devices have still limited resolution and provide only IR intensity information.

Data fusion focuses on a fast algorithmic approach to combine high resolution RGB images with PMD distance data, acquired using a binocular camera setup. The resulting combined RGBZ-data not only enhances the visual result, but also represents a basis for advanced data processing in e.g. object recognition with sub-pixel accuracy. A simple but efficient method is used to detect geometric occlusion caused by the binocular setup, which otherwise will lead to false color assignments.

Motion Compensation

Utilizing current Photo Mixing Detectors to acquire dynamic scenes introduces new problems in terms of motion artefacts. A phenomenon which is based on the internal imaging process of current Photo Mixing Detectors, incorporating four raw images to compute one distance image.

This research direction focuses on the design of accurate algorithms for the realtime compensation of motion artefacts.

Geometric Reconstruction

Current Photo Mixing Detectors acquire simple point clouds representing the scene from the observers point of view.

Geometric reconstruction focuses on the accurate visualization and the reconstruction of observed sceneries incooperating resolution refinement, GPU-based surface estimation as well as object segmentation.

Publications

Articles in Journals

Peer-Reviewed Papers